Systems and Methods for Extreme Temperature Autonomous Drones

This present application is a continuation of United States Non-Provisional Patent Application Ser. No. 19/073,804 filed on Mar. 7, 2025. Applicant claims priority to and the benefit of such application and incorporates the entire application herein by reference in its entirety.

Generally robotic platforms are designed to perform repetitive tasks in various environments, including in refrigerated environments, without tiring and to reduce errors and increase efficiency.

The present disclosure is directed to controlling one or more robotic platforms and computing systems within complex environments. For example, the present disclosure describes autonomous drone technology functionality in refrigerated environments and other special temperature environments. As the drones travel and/or work in refrigerated environments, certain components (e.g., battery) may drop in temperature over time. It may be beneficial to maintain a temperature of batteries of the drones above a certain minimum temperature threshold. Drone charging stations may be outfitted with one or more heating elements to warm the batteries and/or other components of the drones.

Drones may be deployed to exist in certain environments for extended periods of time and often rely on battery power during operation. For example, drones may need to interact with heavy objects in a warehouse environment that may be stored on pallets, on the floor of the warehouse environment, on inventory shelving units (e.g., racks), etc. These objects may be stored in refrigerated or other unique temperature environments. Drones may be tasked with passing through various environments each with its own climate.

The operational temperature of a drone battery can be a significant consideration in ensuring satisfactory performance and/or longevity. This can be particularly so in challenging environments, such as refrigerated warehouses. When housed in a warehouse environment, maintaining the battery above a minimum threshold temperature can help mitigate performance degradation, improve charge efficiency, and reduce risks associated with low-temperature-induced effects. Batteries, particularly batteries relying on lithium-ion, exhibit characteristics that are sensitive to temperature fluctuations, making it advantageous to avoid conditions where the battery might be exposed to excessive cold.

For example, at lower temperatures (e.g., below a threshold minimum temperature), the electrochemical processes within the battery may become less efficient. This inefficiency can manifest in reduced charge acceptance, slower discharge rates, and/or diminished energy output. During use, this inefficiency could reduce the drone's operational range, flight duration, and/or response time. In extreme cases, such reduced performance may result in damage to the drone and/or warehouse inventory. By maintaining the battery above a specified temperature, these performance issues may be minimized, supporting consistent and predictable operation.

When a battery is too cold, its internal resistance can rise, potentially leading to higher energy losses and an increased risk of voltage drops during operation. These factors can influence the overall reliability of the battery and, by extension, the drone's functionality in various applications. By contrast, keeping a battery above a minimum threshold temperature may help avoid risks associated with increased internal resistance.

Maintaining the drone's battery within an acceptable temperature range can also maintain and/or enhance the long-term durability of the drone and/or the battery. Prolonged exposure to low temperatures may accelerate aging and/or lead to irreversible capacity loss. Battery cells and/or connections among the battery cells may degrade.

The technology of the present disclosure may provide a number of technical benefits for improvements for computing systems and/or for drone systems. For instance, as noted above, managing temperature of a battery can improve the life of a battery, reduce incidence of malfunction, and increase the reliability of long-term functionality of the drone and/or of the battery. Additionally or alternatively, by properly managing the hardware of the drone, computing resources and/or data may also be preserved that may otherwise be lost due to extreme temperature situations.

As yet another example, the technology of the present disclosure reduces or eliminates the need of traditional inventory counting methods within refrigerated or other unique temperature environments that include manual scanning each item, manipulation of items, and use of heavy machinery such as forklifts that ultimately decrease human error in the counting process and increase safety in warehouse environments.

For example, in an aspect, the present disclosure provides an example computer-implemented method. The method includes receiving a temperature signal from an autonomous drone indicative of a battery temperature of a battery of the autonomous drone. The method includes determining that the battery temperature is within a threshold temperature range. The method includes determining that a distance between the autonomous drone and a reference point associated with a charging station is within a proximity threshold. The method includes directing heated air to the battery. Directing the heated air be based on determining that the battery temperature is within a threshold temperature range and on determining that the distance between the autonomous drone and the reference point associated with the charging station is within the proximity threshold.

In some embodiments, the method includes delivering power to charge the battery.

In some embodiments, delivering the heated air to the battery includes heating a heating element and driving a fan to pass air through the heating element.

In some embodiments, the method includes downloading, from the autonomous drone, one or more of: inventory data, localization data, map data, or operation data.

In some embodiments, the method includes using a dehumidifier to reduce a moisture content associated with an electronics system of the autonomous drone.

In some embodiments, determining that the distance between the autonomous drone and the reference point associated with the charging station is within the proximity threshold includes determining that the autonomous drone is on a surface associated with a heating element.

In some embodiments, the method includes monitoring, using a plurality of temperature sensors, a temperature of the heated air directed to the battery.

In an aspect, the present disclosure provides an example computing system. The computing system includes one or more processors and one or more non-transitory computer-readable media storing instructions that when executed cause the computing system to perform operations. The operations include receiving, from an autonomous drone, a temperature signal indicative of a battery temperature of a battery of the autonomous drone. The operations include determining, based on the temperature signal, that the battery temperature is outside a threshold temperature range. The operations include determining that a distance between the autonomous drone and a reference point associated with the computing system is within a proximity threshold. The operations include directing heated air to the battery, based on determining that the battery temperature is outside the threshold temperature range and on determining between the autonomous drone and the reference point associated with the computing system is within the proximity threshold.

In some embodiments, the operations further comprise delivering power to charge the battery.

In some embodiments, delivering the heated air to the battery comprises heating a heating element and driving a fan to pass air through the heating element.

In some embodiments, the operations further comprise downloading, from the autonomous drone, at least one of: inventory data, localization data, map data, or operation data.

In some embodiments, the operations further comprise activating a dehumidifier configured to reduce a moisture content associated with an electronics system of the autonomous drone.

In some embodiments, the system further comprises a surface configured to support a weight of the autonomous drone, wherein determining that the distance to the reference point of the autonomous drone is within a proximity threshold comprises determining that the autonomous drone is on the surface.

In some embodiments, the system further comprises a plurality of temperature sensors configured to monitor a temperature of the heated air directed to the battery.

In some embodiments, the system further comprises the autonomous drone within a refrigerated environment, the autonomous drone comprising the battery and one or more battery temperature sensors.

In an aspect, the present disclosure provides an autonomous drone comprising a battery, one or more battery temperature sensors configured to generate a temperature signal indicative of a battery temperature, one or more processors, and one or more non-transitory computer-readable media storing instructions that are executable by one or more processors to perform operations. The operations include generating, using the one or more battery temperature sensors, the temperature signal indicative of the battery temperature. The operations include determining, based on the temperature signal, that the battery temperature is below a threshold temperature range. The operations include routing, based on determining that the battery temperature is outside a threshold temperature range, the autonomous drone to a charging station.

Other example aspects of the present disclosure are directed to other systems, methods, vehicles, apparatuses, tangible non-transitory computer-readable media, and devices for performing functions described herein. These and other features, aspects and advantages of various implementations will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate implementations of the present disclosure and, together with the description, serve to explain the related principles.

The following describes the technology of this disclosure within the context of an autonomous drone within a warehouse environment for example purposes only. As described herein, the technology described herein is not limited to an autonomous drone and may be implemented for or within other warehouse equipment (e.g., forklifts, etc.) and other computing systems in one or more other types of environments.

Operating autonomous drones in cold-chain environments, such as those maintaining refrigerated or freezing temperatures, presents unique challenges. Cold-chain environments are useful for storing and managing temperature-sensitive products such as ice cream, frozen foods, pharmaceuticals, and/or biological samples. These goods can require precise environmental control to prevent spoilage. Despite their efficiency in other applications, drones face several operational and engineering challenges in such environments, including battery performance degradation, condensation management, and the need for effective inventory monitoring.

Cold-chain environments can be maintained at temperatures ranging from just above freezing (0° C. to 10° C. for chilled goods) to sub-zero conditions (−10° C. or lower) for frozen items like ice cream. These environments can affect mechanical components, degrade battery efficiency, and cause condensation issues when moving between temperature zones.

Batteries, such as lithium-ion batteries experience slowed chemical reactions in colder environments. Prolonged exposure to sub-zero conditions can also result in permanent damage to battery cells, significantly reducing their lifespan. Moreover, charging batteries directly in cold docks can create its own challenges. At low temperatures, the lithium ions within the battery move more slowly, increasing the likelihood of lithium plating, which is where lithium metal deposits on the anode. This process can reduce battery capacity, increase the risk of short circuits, and/or accelerate degradation, any of which can raise safety concerns.

Autonomous drones may not remain only in cooler temperatures but may transition between cooler and warmer environments. Condensation occurs when a drone transitions between cold storage and ambient environments. Moist air from warmer environments comes into contact with the cold surfaces of the drone, which can cause water droplets to form on components of the drone. This moisture can lead to electrical short circuits, corrosion of metal parts, and/or degraded sensor performance. Circuit boards and sensors can be particularly susceptible to damage from condensation. Additionally or alternatively, repeated exposure to condensation can cause rust and degradation within the electrical components, including the battery. Embodiments described herein can address many of these challenges and increase the lifespan of the battery and the drones, reduce chances of failure or mistake, and/or mitigate damage to the drone components described herein.

1 6 FIGS.- 1 FIG. 100 101 107 111 100 100 100 100 With reference to, example embodiments of the present disclosure are discussed in further detail.is a block diagram of an example computing system of an autonomous drone according to example implementations of the present disclosure. The example autonomous dronecan include a number of subsystems for performing various operations. The subsystems may include a sensor suite, autonomy system, and control devices. The autonomous dronemay be any type of aerial vehicle configured to operate within a warehouse environment. For example, the autonomous dronemay be a vehicle configured to autonomously perceive and operate within the warehouse environment. This can include multi-rotor drones, fixed-wing drones, single-rotor drones, or fixed-wing hybrid VTOL (e.g., vertical take-off landing) drones. The autonomous dronemay be an autonomous vehicle that can control, be connected to, or be otherwise associated with implements, attachments, and/or accessories for scanning inventory items within a warehouse environment. For example, the autonomous dronemay include a forklift or other ground-based vehicle.

107 107 The autonomy systemcan be implemented by one or more onboard computing devices. This can include one or more processors and one or more memory devices. The one or more memory devices can store instructions executable by the one or more processors to cause the one or more processors to perform operations or functions associated with the subsystems. The computing resources of the autonomy systemcan be shared among its subsystems, or a subsystem can have a set of dedicated computing resources.

100 101 102 103 104 105 106 The example autonomous dronemay include a sensor suitewhich can include different subsystems for performing various sensory operations. The subsystems may include graphics processors, indoor positioning sensors, optical sensors, additional sensors(e.g., LiDAR, RADAR, laser scanner, photodetector array, etc.), and cameras(e.g., wide angle cameras, narrow angle cameras, etc.).

102 103 The graphics processorcan perform image processing of captured images; indoor positioning sensorscan include a variety of sensors (e.g., camera vision based SLAM positioning system employing one or more monocular cameras, one or more stereoscopic camera, one or more laser depth sensors, one or more LIDAR devices, laser and/or ultrasonic rangefinders, an inertial sensor based positioning system, an RF/WIFI/Bluetooth triangulation based sensor system, or the like).

102 100 In some examples, the graphics processorcan include a graphics processing unit (GPU). In some examples, the graphics processing unit can include a graphics card (e.g., board that incorporates the graphics processing unit). In some examples, the graphics card can be integrated into a computing system of the autonomous drone.

102 102 100 102 115 100 In some examples, the graphics processorcan accelerate real-time 3D graphics applications. For example, the graphics processorcan accelerate real-time 3D graphics for the machine-learned models of an autonomous drone. In some examples, the graphics processorcan process sensor datacaptured by an autonomous droneas it flies throughout a warehouse.

104 104 104 104 106 104 Optical sensorscan detect inventory identifiers (e.g., inventory barcodes) and implement optical character recognition (OCR), machine learning, computer vision, or any other image processing algorithm(s)), any combination thereof. In some examples, optical sensorscan be electronic detectors that convert or change light into an electric signal. For example, optical sensorscan utilize electric signals to identify inventory items through obtaining an image of a barcode. In some examples, optical sensorscan be integrated into a camera. In other examples, optical sensorscan be a standalone sensor.

105 Additional sensorscan include a variety of sensors (e.g. temperature sensors, inertial sensors, altitude detectors, LIDAR devices, laser depth sensors, radar/sonar devices, wireless receivers/transceivers, RFID detectors, etc.).

106 106 106 106 106 106 In some examples, camerascan include a varied field of view. In some examples, a wider field of view cameracan observe more of the surrounding environment. In some examples, a narrower field of view cameracan observe less of the surrounding environment. In other examples, the camera lens, focal length, and sensor size can determine the field of view for the camera. In some examples, the field of view for a cameracan be static (e.g., does not change). In other examples, the field of view for a cameracan be dynamic (e.g., can be automatically adjusted).

106 100 101 115 100 101 Camerascan collect wide field of view and narrow field of view images for processing. In the example autonomous drone, the sensor suitecan obtain any sensor datathat describes the surrounding warehouse environment of the autonomous drone. The computing resources of the sensor suitecan be shared among its subsystems, or a subsystem can have a set of dedicated computing resources.

100 107 100 107 108 109 110 The example autonomous dronemay include an autonomy systemwhich can include different subsystems for performing various autonomy operations. The autonomy operations can include perceiving the surrounding environment of the autonomous droneand autonomously planning the drone's motion through the environment, without manual human input. The subsystems of the autonomy systemcan include a drone localization system, flight planning system, and drone control system.

107 107 The autonomy systemcan be implemented by one or more onboard computing devices. This can include one or more processors and one or more memory devices. The one or more memory devices can store instructions executable by the one or more processors to cause the one or more processors to perform operations or functions associated with the subsystems. The computing resources of the autonomy systemcan be shared among its subsystems, or a subsystem can have a set of dedicated computing resources.

108 100 108 100 100 108 100 100 The drone localization systemcan determine the location of the autonomous dronewithin the warehouse environment. In some examples, the localization systemof the autonomous dronecan pinpoint its exact location within the warehouse environment based on determining the location of an object in the immediate vicinity of the autonomous drone. In some examples, the localization systemcan determine the location of the autonomous droneby comparing the distance of the autonomous dronefrom an object identified in the surrounding warehouse environment.

109 100 100 109 100 107 The flight planning systemcan determine a trajectory for the autonomous drone. A flight plan can include one or more trajectories (e.g., flight trajectories) that indicate a path for the autonomous droneto follow. A trajectory can be of a certain length or time range. The length or time range can be defined by the computational planning horizon of the flight planning system. A trajectory can be defined by one or more waypoints (with associated coordinates). The way points(s) can be future locations(s) for the autonomous drone. The flight plans can be continuously generated, updated, and considered by the autonomy system.

110 100 100 111 112 113 114 The drone control systemcan translate the trajectory into vehicle controls for controlling the autonomous drone. For example, the autonomous dronemay include control deviceswhich can include different subsystems for performing various flight control operations. The subsystems may include flight controllers, motors, and propellers.

110 111 110 111 110 112 113 114 107 111 111 112 113 114 In some examples, the drone control systemcan translate the trajectory into electrical signals. In some examples, the control devicescan receive the electrical signals from the drone control system. The control devicescan be configured to implement the translated controls (e.g., electrical signals) from the drone control system. The flight controllercan implement operations to drive the motorsand propellers. In some examples, the autonomy systemcan output instructions that can be received by the control devices. In some examples, the control devicescan translate the instructions into control signals to control the flight controllers, motors, and propellers.

116 100 116 100 100 116 100 Mission datacan be transferred to and from the autonomous dronewith data and instructions for warehouse inventorying. Mission datacan be processed by the autonomous droneand its subsystems as input to the autonomous dronefor autonomous flight operations and the warehouse inventory management process. Example mission datacan include instructions for the autonomous droneto count inventory items stored within the warehouse.

100 115 101 107 107 100 110 111 115 116 As further described, the autonomous dronecan obtain sensor datathrough the sensor suiteand utilize its autonomy systemto detect objects and plan its flight plan to navigate through the warehouse environment. The autonomy systemcan generate control outputs for controlling the autonomous drone(e.g., through drone control systems, control devices, etc.) based on sensor data, mission data, or other data.

2 FIG. 100 116 116 200 200 100 is a block diagram of an example computing ecosystem for an example autonomous drone and an example landing pad, according to some implementations of the present disclosure. As further described herein, the autonomous dronecan receive or transmit mission datawhich include data and instructions for autonomous flight operations and the warehouse inventory management process. The mission datacan be received or transmitted from a landing pad. Landing padscan be a landing surface for an autonomous dronepositioned within the warehouse environment.

200 100 200 200 200 100 100 200 200 100 200 200 100 The example landing padcan be any landing surface suitable for supporting an autonomous drone. In some examples, the landing padis affixed to an inventory shelving unit. In some examples, the landing padis affixed to other warehouse infrastructure. The landing padcan be configured to provide charging power and/or temperature modification (e.g., warm air) to the autonomous dronewhile the autonomous droneis docked on the landing pad. In some examples, the landing padcan provide an accommodating physical shape to one or more portions of autonomous droneto allow for easier landing and docking. In other examples, the landing padcan include visual identifiers to allow for easier detection of the landing padby an autonomous drone.

116 100 200 200 116 100 200 100 200 116 200 100 200 116 200 In an example, mission datacan be received or transmitted between the autonomous droneand landing pad. For example, when a new inventory mission has been generated, the landing padcan transmit mission datato an autonomous dronethat is docked on the landing pad. In some examples, an autonomous dronethat has completed an inventory mission can dock on a landing padand transmit updated mission data(e.g., indicating inventory items that were counted) to the landing pad, as will be further described herein. In some examples, an autonomous dronecan dock on a landing padprior to completing an inventory mission and transmit updated mission datato the landing pad.

116 100 200 201 202 203 204 201 201 201 201 Mission datacan include different types of datasets associated with warehouse inventorying and/or health of the ecosystem of the autonomous droneand/or landing pad. The datasets can include map data, location data, inventory data, and/or temperature data. The map datacan include a dimensional (e.g., 2D, 3D, 4D, etc.) layout of the warehouse environment. In some examples, the map datacan be generated by manually mapping the layout of the warehouse using LiDAR and camera sensors. In some examples, map datacan be generated by manually flying a drone throughout the warehouse environment. In some examples, the map datacan be generated by processing a facility map of the warehouse which includes dimensional measurements of the warehouse and warehouse infrastructure. Warehouse infrastructure can include any stationary or mobile object within a warehouse. In some examples warehouse infrastructure can include inventory shelving units, large ceiling fans, cranes or hoists, integrated dock levelers, work benches, etc.

201 201 100 In some examples, map datacan include information indicative of one or more obstacles within the warehouse environment. For example, the map datamay encode the locations of one or more obstacles. This information may be included as an “obstacle map”. An obstacle map can include known or perceived obstacles which can disrupt a flight plan for an autonomous drone. Obstacles can include pallets, utility carts or dollies, totes, bins, etc.

100 100 In some examples, the obstacle map can be generated by manually mapping the layout of the warehouse using LiDAR, camera, or other sensors. In some examples, the obstacle map can be generated by processing a facility map of the warehouse which includes dimensional measurements of warehouse infrastructure. In some examples, an obstacle map can be updated by an autonomous dronethat perceived the obstacle during an inventory mission. In other examples, an obstacle map can be updated by an autonomous dronethat perceived a removed obstacle.

202 200 100 200 100 200 116 100 200 200 100 116 100 202 100 100 100 200 Location datacan include a current location of the landing pad. In some examples, the autonomous dronecan be docked on a landing pad. For example, when the autonomous droneis docked on a landing pad, mission datacan be transmitted between the autonomous droneand landing padupon contact. In some examples, the landing padcan charge the autonomous dronewhile mission datais being transmitted. In some example implementations, when an autonomous dronecomes online, and upon initializing sensors, location datacan be transmitted to the autonomous droneto provide a current location of the autonomous drone. In some example implementations, the current location of the autonomous droneis the location of the landing padwithin the warehouse environment.

202 202 202 202 201 202 In some examples, the location datacan include the region (e.g., slots, etc.) of the warehouse where inventory items are located. For example, the location datacan include the location of a set of slots or inventory shelving units where inventory items are located. As used herein, “slot” can refer to an area or volume defined by two or more dimensions. In some examples, the location datacan be an associated location on a dimensional layout of the warehouse. In some example implementations, the location datacan include map data. In other examples, location datacan include the location of obstacles within the warehouse environment.

203 100 203 203 203 Inventory datacan include relevant inventory items to be counted by the autonomous drone. For example, inventory datacan include a list of inventory items expected to be within the warehouse. In some implementations, inventory datacan include data indicative of where an inventory item is expected to be located in a specific slot or inventory shelving unit. In some examples, the inventory datacan be a database table including a plurality of rows and columns. In some examples, the database table can include the slot on the inventory shelving unit where the inventory item should be located, a description of the inventory item, the barcode identifier, etc., in the columns and rows. In some examples, the database table can be compressed. In other examples, the database table can be updated as new inventory data (e.g., inventory items leave or enter the warehouse) is generated.

203 100 In other examples, inventory datacan include a list of missing inventory items. Missing inventory items can include inventory items which cannot be found by the autonomous dronein their expected location or which have not been counted. In some examples, missing inventory items may have already left the warehouse. In other examples, missing inventory items may be lost.

204 100 200 204 100 204 100 204 200 100 Temperature datacan include information about the thermal conditions of the autonomous drone, the landing pad, and/or the surrounding warehouse environment. For example, temperature datacan represent the current temperature of a battery of the autonomous droneand/or a temperature of other components, such as motor components, and/or sensitive electronics. This temperature datacan be used to monitor and maintain optimal operating conditions for the autonomous drone. In some implementations, temperature datais collected in real-time during flight or while docked on the landing pad, allowing for adaptive adjustments to ensure the operational efficiency and longevity of the autonomous drone.

200 204 204 200 424 4 FIG. The landing padcan use temperature datato manage temperature-related processes. For example, if temperature dataindicates that the drone's battery is below a minimum threshold temperature, the landing padmay activate a heating element (e.g., heating elementof), such as a warm airflow system, to raise the battery temperature before the next flight. A warmer battery can help ensure that the electrochemical processes within the battery occur efficiently, reducing the risk of diminished flight performance or potential damage caused by operating at suboptimal temperatures.

204 204 200 100 200 In addition to maintaining the operational readiness of the drone, temperature datacan contribute to predictive maintenance within the warehouse ecosystem. For instance, recurring patterns in temperature fluctuations detected through temperature datacan indicate potential wear or degradation in the drone's components or the charging system of the landing pad. By analyzing temperature trends, the autonomous droneand/or the landing padcan generate alerts or recommendations for preventive maintenance, reducing the likelihood of mission interruptions caused by equipment failures.

204 204 116 200 100 Temperature datacan also be used in flight planning and execution. For example, if the warehouse environment is subject to significant temperature variations due to seasonal changes and/or temperature fluctuations within different parts of the warehouse (e.g., in a refrigerated vs nonrefrigerated environment), temperature datacan be integrated into the mission datato adjust flight schedules or select alternative landing padsequipped with temperature management features. This adaptability enables the autonomous droneto operate efficiently across a range of environmental conditions, thereby enhancing the reliability of the overall warehouse inventory management system.

115 200 100 116 100 115 115 200 In an embodiment, sensor datamay be transmitted to the landing pad. For instance, the autonomous dronemay receive mission datainstructing the autonomous droneto initiate a flight plan to capture sensor dataof a plurality of slots in region of the warehouse to facilitate the counting of inventory items in the region. In an embodiment, the sensor datamay be captured and transmitted to the landing padfor offline processing.

203 201 202 201 203 202 In an embodiment, inventory datacan include map dataand location data. In other examples, missing inventory items can be included in map data. In some examples, missing inventory items can update inventory data. In some examples, missing inventory items can update location data.

203 203 203 Inventory datacan be generated by a warehouse inventory management software. For example, warehouse employees can update an inventory management software with current inventory items. In some examples, the inventory management software can track the volume and location of inventory items within the warehouse. In some examples, the inventory management software can be updated as inventory items enter and leave the warehouse. In some examples, inventory datacan synchronize with the inventory management software to maintain accurate inventory levels. In other examples, inventory datacan update the inventory levels in the inventory management software.

203 100 100 203 203 203 203 203 Inventory datacan be updated by an autonomous drone. For example, as the autonomous dronemoves (e.g., flies, drives on the ground) throughout the warehouse to scan inventory, inventory items may be counted to maintain an updated record of inventory items in the warehouse at any point in time. When inventory items are not found, inventory datacan be updated to reflect the current stock levels of current inventory within the warehouse. In some examples, inventory items may be located in a different location than the inventory data. When inventory items are scanned in a different location than the inventory data, the inventory datacan be updated to reflect the current location of the inventory items. In some examples, an inventory management system can be updated by the inventory data.

203 203 203 203 203 In some examples, inventory datacan be updated to reflect misscanned inventory. Misscanned inventory can include inventory items which have an unreadable or obscure barcode. In some examples, inventory datacan include a count and location of miscanned inventory. In some examples, an inventory management system can be updated by the inventory data. In some implementations, inventory datacan be updated to reflect misslots. Misslots can include inventory located in a different location (e.g., slot) than what was indicated in the inventory data. In some examples, a misslot can include inventory items in the wrong location (e.g., slot).

100 200 100 100 200 100 200 100 200 100 100 100 100 200 100 100 200 100 As further described herein, the autonomous droneand landing padcan exchange mission data before, during, and after an autonomous dronehas completed its inventory mission. In some examples, a warehouse can use multiple autonomous dronesand multiple landing padswithin a warehouse. In some examples, multiple autonomous dronescan use different or multiple landing padsto complete its inventory mission. In some examples, multiple autonomous dronescan use the same landing pad. The autonomous dronesmay be able to communicate with each other via respective data interfaces or other communication interfaces. For example a first autonomous dronemay be able to relay data (e.g., any of the described herein) to a second autonomous drone. In some embodiments, the second autonomous dronemay be able to serve as an intermediary, passing information to a landing pador another computing device on behalf of the first autonomous drone. Additionally or alternatively, the second autonomous dronemay be able to receive a response from the computing device (e.g., the landing pad) and relay the response back to the first autonomous drone.

3 FIG. 300 100 300 302 300 300 is a representation of an example autonomous drone flight plan through a warehouse environment, according to some implementations of the present disclosure. As further described herein, the autonomous dronecan navigate the warehouse environmentto count inventory items. A warehouse environmentcan be any building or structure where manufactured goods or raw materials may be stored. In some examples, the warehouse environmentmay include an indoor environment (e.g., within one or more facilities, etc.) or an outdoor environment. An indoor environment, for example, may be an environment enclosed by a structure such as a building (e.g., a service depot, maintenance location, manufacturing facility, etc.). An outdoor environment, for example, may be one or more areas in the outside world such as, for example, one or more rural areas suitable for storage of manufactured goods or raw materials (e.g., supply chain port, lumber yards, etc.).

300 301 302 301 300 301 301 301 301 100 300 The warehouse environmentmay include inventory shelving units(e.g., inventory storage racks) which include a plurality of slots for storing the inventory items. The inventory shelving unitsmay be positioned in a predictable and repeatable pattern throughout the warehouse environment. In some examples, the inventory shelving unitscan be positioned in rows. In other examples, the inventory shelving unitscan be positioned adjacent to each other. In some examples the inventory shelving unitscan be stacked on each other. In some examples, the inventory shelving unitscan be positioned to allow for people or autonomous dronesto navigate the warehouse environment.

301 301 10 301 301 301 feet The inventory shelving unitscan be of standard warehouse rack size or of custom size. In some examples, the inventory shelving unitscan be 8-feet,-, 12-feet, 16-feet, and 20-feet upright. In other examples, the inventory shelving unitscan be of a custom size (e.g., 11-feet, 11.5-feet, etc.). In some examples, the inventory shelving unitscan be based on the measure and height of inventory pallets. In other examples, the inventory shelving unitscan be based on the racking beam size.

301 302 302 300 302 302 302 302 302 The inventory shelving unitscan store warehouse inventory itemsin slots on its shelves. An inventory itemcan be any manufactured product or raw material which is being stored in the warehouse environment. For example, inventory itemscan include boxes which contain a manufactured good or raw material. In some examples, inventory itemscan include other packaged or wrapped (e.g., storage wrapped) items. In some examples, inventory itemscan include bins or totes that store a manufactured good or raw material. In other examples, inventory itemsmay not be packaged in any box, wrapping or storage material. In some examples, inventory itemsinclude an identifier (e.g., barcode).

302 301 302 301 300 100 Inventory itemscan be stored directly in slots on an inventory shelving unitor on pallets. For example, inventory itemsmay be tightly coupled with other similar items and stored on an inventory pallet for easy storage and retrieval. In some examples, inventory pallets may be stored on inventory shelving units. In other examples, inventory pallets may be stored on the floor of the warehouse environment. For instance, inventory pallets that are stored on the warehouse floor may be identified as an obstacle for an autonomous drone. In some examples, inventory pallets stored on the warehouse floor may be captured in a warehouse dimensional layout.

302 301 300 302 301 In some examples, inventory itemsmay be bulk items. Bulk items may include objects which are too large to fit on an inventory shelving unit. Example bulk items may include large appliances, heavy equipment, or other bulky items. The bulk objects may also be stored in designated slots on the floor of the warehouse environmentand may be counted in a similar manner to other inventory itemsstored in slots on inventory shelving units.

300 301 200 200 301 200 301 100 200 301 200 301 100 200 In the example warehouse environment, inventory shelving unitscan support landing pads. In some examples, the landing padscan be affixed to an end of the inventory shelving unit. For example, landing padsaffixed to an end of the inventory shelving unitallow for more takeoff and landing space for an autonomous drone. In some examples, the landing padis affixed towards the top level of the inventory shelving unit. For example, affixing the landing padtowards the top level of the inventory shelving unitcan ensure that people or warehouse machinery do not collide with the autonomous droneor landing pad.

100 303 300 100 116 100 303 303 116 303 100 303 303 200 The example autonomous dronecan execute a flight planto navigate the warehouse environment. For example, when an autonomous dronereceives mission data, the autonomous dronecan determine a flight planto execute the inventory mission. In some examples, a flight plancan be determined based on the mission data. In some examples, the flight plancan be generated by the autonomous drone. In other examples, the flight plancan be generated remotely. In some examples, the flight plancan be transmitted from the landing pad.

303 100 300 100 100 100 100 300 100 303 303 300 The flight plancan be updated as the autonomous droneflies throughout the warehouse environment. For example, the autonomous dronecan encounter an obstacle as it executes its inventory mission. In some examples, the autonomous dronecan execute active avoidance to avoid the obstacle. Active avoidance can include avoidance maneuvers executed by the autonomous droneto avoid obstacles. In some examples, active avoidance can prevent the autonomous dronefrom colliding with an object in the warehouse environment. In some examples, the autonomous dronecan generate an updated flight planto complete its inventory mission following the avoidance of an obstacle. In some examples, a flight plancan account for known obstacles in the warehouse environment.

303 100 100 109 303 100 100 115 100 303 100 116 303 100 115 116 303 The flight plancan optimize the travel time and distance for an autonomous drone. For instance, the autonomous dronecan use the flight planning systemto generate the most efficient flight planfor the autonomous droneto execute its inventory mission. In some examples, the autonomous dronecan use sensor dataperceived by the autonomous droneto determine the most efficient flight plan. In some examples, the autonomous dronecan use mission datato determine the most efficient flight plan. In other examples, the autonomous dronecan use both sensor dataand mission datato generate and optimize the flight plan.

100 300 302 301 303 200 100 300 303 As further described herein, the autonomous dronecan traverse the warehouse environmentto scan inventory itemsstored on inventory shelving unitsby executing a motion plan (e.g., flight plan) and docking on a landing pad. In some examples, multiple autonomous dronescan traverse the warehouse environmentby executing respective motion plans (e.g., flight plans) concurrently.

While aerial drones are described and illustrated herein for exemplary purposes, it should be understood that this is by way of example only and is not intended to be limiting. The described systems, methods, and components can be equally applicable to other types of drones, including but not limited to ground-based, water-based, and/or hybrid drones capable of operating across multiple terrains. The scope of the invention is intended to encompass any autonomous or semi-autonomous vehicles capable of utilizing the disclosed features, regardless of their operational environment or mode of movement.

4 FIG. 2 FIG. 1 FIG. 400 100 200 400 100 200 100 404 408 416 416 404 101 107 111 408 404 408 416 416 408 408 416 416 a b a b a b depicts a schematic of an example temperature management system, according to some implementations of the present disclosure. The example temperature management systemshows an example embodiment of the autonomous droneand landing padshown in. The temperature management systemcan include an autonomous droneand a landing pad. The autonomous dronecan include one or more drone systems, a battery, and one or more battery temperature sensors-. The drone systemsmay correspond to one or more of the systems described above with respect to, such as the sensor suites, autonomy systems, and/or the control devices. The batterycan be configured to power the drone systems. In cooler environments, the batterymay not be able to perform at the same high level of performance as in a warmer environment. Accordingly, the battery temperature sensors-can be configured to monitor a temperature of a corresponding location of the battery, such as a cell of the battery. In some embodiments, each of the battery temperature sensors-may be associated with respective battery cells.

100 200 404 404 436 436 100 404 436 436 200 1020 404 1010 436 1000 436 200 6 FIG. 6 FIG. 6 FIG. The autonomous dronemay be configured to communicate with the landing padusing the drone systems. The drone systemsmay be configured to communicate with the computing system, for example remotely. The computing systemmay include a data interface, such as a wireless data interface, that can communicate with the autonomous dronevia the drone systems. The computing systemmay include a memory and/or one or more storage devices. For example, the computing systemmay include one or more features of the landing paddiscussed in(e.g., the computing device(s)). Additionally or alternatively, the drone systemscan include one or more features of the computing device(s)discussed in. In some embodiments, the computing systemmay include a remote computing system that includes one or more features of the computing systemdiscussed in. For example, in some embodiments, the computing systemmay be remote from the landing pad.

404 404 436 408 404 416 416 a b The computing system can transmit data to and/or receive data from the drone systems. For example, the drone systemscan transmit data to the computing systemrelated to a temperature of the battery. The drone systemsmay receive temperature readings from the battery temperature sensors-and transmit a temperature signal based on these temperature readings.

436 100 200 436 436 408 436 100 200 436 200 100 436 100 404 100 200 100 200 408 In some embodiments, the computing systemmay determine that a distance between the autonomous droneand the landing pad(and/or some other reference point associated with the computing system). The computing systemmay determine that the distance is within an acceptable proximity to begin to warm the battery. For example, the computing systemmay determine that the autonomous droneis on a surface of the landing pad. The computing systemmay determine this in part due to an electrical communication between the charging system of the landing padand the autonomous drone. Additionally or alternatively, the computing systemmay receive a data transmission from the autonomous drone(e.g., from the drone systems) that the autonomous droneis landed on the landing pad. This indication from the autonomous dronemay trigger activation of one or more elements of the landing paddescribed herein to charge and/or warm the battery.

404 408 303 408 404 408 408 404 408 408 416 416 408 a b In some embodiments, the drone systemsmay determine a temperature change of the batteryas a result of the autonomous drone traversing the flight plan. The battery's temperature change may be estimated based on information related to the battery. The drone systemsmay determine the temperature change of the batteryby considering the number of charge cycles, overall age, or similar characteristics of the battery. The drone systemsmay establish a target temperature and/or target minimum threshold temperature for the battery. The target temperature and/or target minimum threshold temperature may be determined in part by a temperature change in the battery, such as that measured by the battery temperature sensors-. This target temperature and/or target minimum threshold temperature may be set so as to reduce, minimize, or even eliminate the duration that the batteryoperates outside a target temperature range during the flight. “Battery information” can be understood broadly to include parameters such as battery age, current temperature, charge/discharge cycles, maximum and minimum achieved temperatures, chemical composition, number and type of cells, insulation type or rating, rated capacity, and actual capacity.

404 200 436 408 200 424 428 420 420 436 408 408 200 424 428 400 408 a b The drone systemcan communicate with the landing pad(e.g., via the computing system) to warm the batteryto the target temperature and/or above the target minimum threshold temperature. For example, the landing padcan include a heating element, a fan, one or more station temperature sensors-, and/or the computing system. The batterymay include channels or ducting that enable air to flow through, over, or around portions of the battery(e.g., cells thereof) to modify their temperature. The landing padmay cause one or more control devices, such as the heating elementand/or the fan, and/or similar components to achieve the target temperature and/or achieve a temperature above the target minimum threshold temperature. Thermal regulation operations can be adjusted by the temperature management systemto more precisely heat the batteryas needed.

428 432 424 432 432 408 416 416 420 432 428 424 420 424 a b a b The fancan generate an airflowthat can be directed past (e.g., through, around, over, etc.) the heating elementin order to warm the airflow. The airflowcan be passed through the batteryand/or the one or more battery temperature sensors-. The first station temperature sensorcan sense a temperature of the airflowproduced by the fanand passed through/around/over the heating element. Additionally or alternatively, the second station temperature sensorcan sense a temperature of ambient air that has not been heated by the heating element.

424 428 432 408 100 436 424 420 420 432 436 424 432 436 424 408 a a In some embodiments, the heating elementcan generate heat and warm up air that is driven by the fanto drive the air in the airflowvertically to warm the batteryof the autonomous drone. The computing systemmay adjust the heating elementbased on data from the first station temperature sensoralone. For example, if the first station temperature sensordetects that the temperature of the outgoing airflowremains consistently below the target (e.g., predefined) temperature threshold at a time or over a threshold amount of time, the computing systemmay increase the power supplied to the heating elementto generate more heat. Additionally or alternatively, if the outgoing airflowexceeds a maximum allowable temperature (e.g., of a target temperature range), the computing systemmay decrease the power to the heating elementto prevent overheating of the batteryor other components.

424 428 420 420 436 424 b b Adjustments to the heating elementor fanmay also be informed by readings from the second station temperature sensoralone. For example, if the ambient air temperature detected by the second station temperature sensordrops below an expected temperature level, the computing systemmay preemptively increase the output of the heating elementto compensate for the greater heat loss caused by the colder ambient environment. Similarly, if ambient air temperature rises, the system may reduce heating to conserve energy and/or prevent unnecessary heat buildup.

420 420 436 420 420 432 424 436 424 432 a b b a Additionally or alternatively, using a combination of readings from the first and second station temperature sensors-may provide valuable data for calculating differential temperature values. The computing systemmay subtract the temperature reading from the second station temperature sensor(ambient air) from that of the first station temperature sensor(outgoing airflow) to determine the heating efficiency. A differential beyond a target range may indicate that the heating elementpower may be reduced. Additionally or alternatively, a differential below a target range may result in the computing systemcausing the heating elementto generate a warmer airflow.

416 416 100 200 408 424 428 436 432 428 424 408 436 424 428 a b The battery temperature sensors-onboard the autonomous dronecan offer feedback about the effectiveness of the heating system of the landing pad. For example, if the batterytemperature readings remain below the target temperature and/or the target temperature range (e.g., despite adjustments to the heating elementand fan), the computing systemmay increase airflowfrom the fanand/or heating output from the heating element. Conversely, if the batteryachieves the target temperature and/or target temperature range, and/or if the temperature exceeds the target temperature range, the computing systemcan cause the heating elementto reduce heat generation and/or cause the fanto reduce speed to reduce airflow velocity.

436 200 436 436 424 428 436 424 428 408 In some embodiments, the computing systemcan receive instructions external to the landing pad. For example, the computing systemmay receive manual input and/or data from other sensors or systems (e.g., via a wireless communication system). For example, an operator might input a desired battery temperature range, prompting the computing systemto adjust the heating elementor fanaccordingly. Additionally or alternatively, external environmental sensors and/or predictive models may inform the computing systemabout upcoming changes in ambient conditions, enabling proactive adjustments to the heating elementand/or fanto maintain and/or achieve a target temperature of the battery.

100 200 100 408 100 200 436 424 428 436 424 432 424 432 Additionally or alternatively, one or more condensation sensors and/or humidity sensors may be present in the autonomous droneand/or the landing pad. The condensation sensors can be configured to sense a level of condensation present on the autonomous droneand/or a portion thereof (e.g., the battery). Additionally, or alternatively, the humidity sensors can reduce a likelihood of condensation forming on one or more parts of the autonomous droneand/or the landing pad. For example, if the relative humidity is detected to be above a threshold humility, then heat can be applied and/or air circulated using a fan to reduce and/or avoid condensation accumulation and/or to ventilate the electronics. The computing systemcan receive readings from the condensation sensors and modify the heating elementand/or the fanbased thereon. For example, if condensation is detected above a threshold level, the computing systemmay direct the heating elementto generate a greater airflowand/or the heating elementto generate a greater heat. Generating greater airflowand/or greater heat may include turning the relevant element(s) on.

436 200 408 408 436 404 408 200 100 200 408 The computing systemmay additionally or alternatively cause the landing padto charge the batterybefore, after, and/or during the thermal conditioning of the battery. Additionally or alternatively, the computing systemmay send signals to other subsystems, such as the drone systems, to indicate readiness for deployment. These subsystems may further manage tasks such as ensuring coupling of the batteryto the autonomous drone and/or providing the landing padand/or other systems with relevant battery status data. For example, the autonomous droneand/or the landing padmay include a user interface that displays details such as charge state, temperature, and/or lifecycle statistics of battery.

436 100 416 416 420 420 408 436 200 408 420 420 408 416 416 436 100 200 200 408 436 100 200 a b a b a b a b The computing systemmay issue instructions to the autonomous dronebased on one or more temperature readings (e.g., from the battery temperature sensors-and/or the station temperature sensors-) to improve performance of the battery. For example, the computing systemcan direct the charging system of the landing padto bring the batteryto a target charge level based on an ambient temperature, based on a temperature differential between those sensed by the station temperature sensors-, and/or a temperature differential among different portions of the batteryas sensed by the battery temperature sensors-. Additionally or alternatively, the computing systemmay instruct an autonomous dronethat is not within a proximity range of the landing padto be heated to return to the landing padto have its batterywarmed. Additionally or alternatively, the computing systemmay send instructions to the autonomous droneto reduce an allowable time away from the landing padand/or otherwise modify a flight plan based on a received temperature reading.

436 404 436 424 428 Additionally or alternatively, the computing systemmay receive expected flight plans and/or instruct the drone systemsto modify a flight plan based on the same. In some embodiments, the computing systemcan instruct the heating elementand/or fanto achieve modified target temperatures based on flight plan demands and/or environmental conditions. Additionally or alternatively, the system can gather pre-and/or post-flight data, such as charge levels, temperatures, and/or in-flight parameters (e.g., discharge rates, ambient temperatures) to refine predictive models for battery performance, such as capacity degradation or temperature changes due to flight profiles.

408 Predicting the temperature change involves analyzing the complete temperature profile of the batteryover the predefined flight path, considering variables like power output, degradation levels, and ambient environmental conditions. For example, degraded batteries may experience higher temperature increases for the same power output compared to newer ones. The system may account for these factors using mathematical relationships, such as ΔT/power ratios, or by referencing ratios correlating battery age and degradation levels with predicted temperature behaviors.

436 100 436 The computing systemmay be able to incorporate external factors, such as ambient air temperature, condensation on the autonomous drone, humidity, and/or autonomous drone velocity, to determine a target temperature and/or target temperature range. By using heat transfer coefficients that reflect insulation properties and air flow dynamics, the computing systemcan adjust predictions based on expected heat gain or loss during specific flight segments. Such comprehensive modeling allows for precise determination of a target initial temperature that can improve battery performance during the mission. The target temperature may be selected to reduce and/or minimize deviations from a flight plan and/or to achieve other specified objectives, as described herein.

100 Certain components of the autonomous drone, such as printed circuit boards (PCBs) and/or the battery housing, may be waterproofed. One or more layers of waterproof coatings, such as conformal coatings and/or encapsulating compounds, and/or other waterproofing materials may be applied to the PCBs and/or other electrical elements to protect against moisture ingress. Additionally or alternatively, a housing of the battery, which may contain one or more PCBs, can include waterproof coatings.

100 408 408 In some embodiments, the autonomous dronemay include one or more active dehumidification systems, such as an electric micro-dehumidifier. The dehumidification system can be configured to remove moisture in and around the batteryand/or a housing thereof. The dehumidification system can use electrical energy to extract water from the air and/or surfaces of electrical components to help them be and/or remain dry. Additionally or alternatively, passive dehumidification system may be used. For example, one or more of desiccants (e.g., silica gel, activated alumina), calcium chloride, zeolite, salts, and/or other materials may be included near electrical components to prevent and/or absorb condensation. Additionally or alternatively, mechanical means may be used to remove condensation. For example, mechanical wipers and/or sweeps may be included to physically remove condensation from exposed components, such as the battery.

436 408 416 416 420 420 408 436 424 428 a b a b In some embodiments, the computing systemcan be configured to identify feedback loops to improve thermal management of the battery. For example, if the battery temperature sensors-and/or the station temperature sensors-detect an acceleration in change in relevant temperature (e.g., temperature of the battery), the computing systemcan modulate activation of the heating elementand/or the fan.

100 408 100 408 In some embodiments, the autonomous droneincludes one or more backup power sources. These backup power sources may be configured to warm the batteryand/or other electrical components of the autonomous dronethat have dropped below a target temperature range/threshold and/or that have accumulated condensation above a threshold. For example, one or more heaters can pre-warm the batteryand/or backup batteries before switching to active use to reduce the risk of power loss due to cold temperatures.

5 FIG. 1 4 FIGS.- 500 500 500 408 depicts a flow chart of an example method, according to some implementations of the present disclosure. One or more portion(s) of the methodmay be implemented by a computing system that includes one or more computing devices such as, for example, the computing systems described with reference to the other figures. Each respective portion of the methodmay be performed by any (or any combination) of one or more computing devices. Moreover, one or more portion(s) of the methodmay be implemented by the hardware components of the device(s) described herein (e.g., as in, etc.), for example, to manage a temperature of a battery (e.g., the battery).

5 FIG. 5 FIG. 500 depicts elements performed in a particular order for purposes of illustration and discussion. Those of ordinary skill in the art, using the disclosures provided herein, will understand that the elements of any of the methods discussed herein may be adapted, rearranged, expanded, omitted, combined, or modified in various ways without deviating from the scope of the present disclosure.is described with reference to elements/terms described with respect to other systems and figures for exemplary illustrated purposes and is not meant to be limiting. One or more portions of methodmay be performed additionally, or alternatively, by other systems.

502 500 436 115 416 416 100 300 115 408 a b Atthe methodmay include receiving, from an autonomous drone, a temperature signal indicative of a battery temperature of a battery of the autonomous drone. For instance, the computing systemmay wirelessly receive sensor data (e.g., sensor data) captured by the battery temperature sensors-of an autonomous droneoperating within the warehouse environment. The sensor datamay include a temperature of the battery.

504 500 436 408 100 100 100 Atthe methodmay include determining, based on the temperature signal, that the battery temperature is outside a threshold temperature range. For instance, the computing systemmay determine that a temperature of the batteryis below an acceptable temperature threshold that is necessary for an upcoming mission. This determination may be further based, for example, on a distance to be traveled by the autonomous drone, a set of environments (e.g., associated temperature changes, associated humidities, etc.) that the autonomous dronewill pass through, a power required by the autonomous drone, etc.

506 500 436 100 200 424 Atthe methodmay include determining that a distance between the autonomous drone and a reference point associated with the computing system is within a proximity threshold. For instance, the computing systemmay determine that the autonomous droneis landed on a surface of the landing padand is ready to charge and/or receive heating from the heating element.

508 500 436 408 408 408 424 428 436 408 200 Atthe methodmay include directing heated air to the battery, based on determining that the battery temperature is outside the threshold temperature range and on determining between the autonomous drone and the reference point associated with the computing system is within the proximity threshold. For instance, the computing systemmay initialize heating of the batteryonce it is determined that the temperature of the batteryneeds to be raised and that the batteryis close enough to the heating elementand/or fanto be effective. Additionally or alternatively, the computing systemmay cause the batteryto be charged by the landing pad.

436 100 200 116 In some embodiments, the computing systemmay download other data from the autonomous dronewhile it is within the threshold proximity of the reference point. For example, the landing padmay download inventory data, localization data, map data, operation data, and/or other data described herein (e.g., any of the mission data).

6 FIG. 10 10 100 200 1002 100 200 100 200 100 200 100 100 200 . is a block diagram of an example computing ecosystem, according to some implementations of the present disclosure. The example computing ecosystemcan include an autonomous droneand a landing padthat are communicatively coupled over one or more networks. In some implementations, the autonomous droneand the landing padcan communicate through a contact connection (e.g., wired ethernet connection) when the autonomous droneis docked on the landing pad. In other implementations, the autonomous droneand the landing padcan communicate over a wireless connection (e.g., wireless local area network (WLAN), wireless wide area network (WWAN), near-field communication, other shorter distance communication protocols, etc.) while the autonomous droneis in-flight. In some implementations, the autonomous droneor the landing padcan implement one or more of the systems, operations, or functionalities described herein for validating one or more systems or operational systems.

1000 100 200 1002 1000 100 200 100 200 1002 1000 1002 100 200 In some implementations, a computing system, the autonomous drone, and/or the landing padcan be communicatively coupled over one or more networks. The computing systemcan be, for example, a cloud-based server system that is remote from the autonomous droneand the landing pad. This may include, for example, a computing system associated with a warehouse, an entity associated with the inventory (e.g., shipper, manager, operator), an entity associated with the autonomous drone(e.g., manufacturer, distributor, operator, maintainer), an entity associated with the landing pad(e.g., manufacturer, distributor, operator, maintainer), etc. In some implementations, one or more of the networksused to communicate with the computing systemmay be different than one or more of the networksused by the autonomous droneand the landing padto communicate with one another.

1010 100 100 1010 100 107 100 1010 108 109 110 1010 100 100 1010 In some implementations, the computing devicescan be included in an autonomous droneand be utilized to perform the functions of an autonomous droneas described herein. For example, the computing devicescan be located onboard an autonomous droneand implement the autonomy systemfor autonomously operating the autonomous drone. In some implementations, the computing devicescan represent the entire onboard computing system or a portion thereof (e.g., the drone localization system, the flight planning system, the drone control system, or a combination thereof, etc.). In other implementations, the computing devicesmay not be located onboard an autonomous drone. In some implementations, the autonomous dronecan include one or more distinct physical computing devices.

100 1010 1011 1012 1011 1012 The autonomous drone(e.g., the computing device(s)thereof) can include one or more processorsand a memory. The one or more processorscan be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memorycan include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and combinations thereof.

1012 1011 1012 1013 1013 115 116 100 100 The memorycan store information that can be accessed by the one or more processors. For instance, the memory(e.g., one or more non-transitory computer-readable storage media, memory devices, etc.) can store datathat can be obtained (e.g., received, accessed, written, manipulated, created, generated, stored, pulled, downloaded, etc.). The datacan include, for instance, sensor data, mission data, data associated with autonomy functions (e.g., data associated with the perception, planning, or control functions), simulation data, or any data or information described herein. In some implementations, the autonomous dronecan obtain data from one or more memory device(s) that are remote from the autonomous drone.

1012 1014 1011 1014 1014 1011 The memorycan store computer-readable instructionsthat can be executed by the one or more processors. The instructionscan be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructionscan be executed in logically or virtually separate threads on the processor(s).

1012 1014 1011 1010 100 For example, the memorycan store instructionsthat are executable by one or more processors (e.g., by the one or more processors, by one or more other processors, etc.) to perform (e.g., with the computing device(s), the autonomous drone, or other system(s) having processors executing the instructions) any of the operations, functions, or methods/processes (or portions thereof) described herein.

100 1015 1015 1015 100 404 In some implementations, the autonomous dronecan store or include one or more models. In some implementations, the modelscan be or can otherwise include one or more machine-learned models (e.g., semantic fusion mode, combinatorial optimization mode, object counting model, etc.). As examples, the modelscan be or can otherwise include various machine-learned models such as, for example, regression networks, generative adversarial networks, neural networks (e.g., deep neural networks), support vector machines, decision trees, ensemble models, k-nearest neighbors models, Bayesian networks, or other types of models including linear models or non-linear models. Example neural networks include feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, or other forms of neural networks. For example, the autonomous dronecan include one or more models for implementing object detection and counting, including the drone systems.

100 1015 1018 200 1002 100 1015 1012 100 1015 1011 100 1015 300 In some implementations, the autonomous dronecan obtain the one or more modelsusing communication interface(s)to communicate with the landing padover the network(s). For instance, the autonomous dronecan store the model(s)(e.g., one or more machine-learned models) in the memory. The autonomous dronecan then use or otherwise implement the models(e.g., by the processors). By way of example, the autonomous dronecan implement the model(s)to detect and count objects in the warehouse environment.

200 1020 200 1021 1022 1021 1022 The landing padcan include one or more computing devices. The landing padcan include one or more processorsand a memory. The one or more processorscan be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memorycan include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and combinations thereof.

1022 1021 1022 1023 1023 115 116 200 The memorycan store information that can be accessed by the one or more processors. For instance, the memory(e.g., one or more non-transitory computer-readable storage media, memory devices, etc.) can store datathat can be obtained. The datacan include, for instance, sensor data, mission data, data associated with a warehouse environment inventory management system, data associated with inventory scanning missions, or any data or information described herein. In some implementations, the landing padcan obtain data from one or more memory device(s) that are remote from the landing pad.

1022 1024 1021 1020 200 1010 100 200 1025 1025 For example, the memorycan store instructionsthat are executable (e.g., by the one or more processors, by one or more other processors, etc.) to perform (e.g., with the computing device(s), the landing pad, or other system(s) having processors for executing the instructions, such as computing device(s)or the autonomous drone) any of the operations, functions, or methods/processes described herein. This can also include, for example, validating a machined-learned operational system. For example, the landing padcan store or include one or more models. In some implementations, the modelscan be or can otherwise include one or more machine-learned models described herein.

200 200 In some implementations, the landing padcan include one or more server computing devices. In the event that the landing padincludes multiple server computing devices, such server computing devices can operate according to various computing architectures, including, for example, sequential computing architectures, parallel computing architectures, or some combination thereof.

100 200 1018 1026 1018 1026 100 200 1018 1026 1002 1018 1026 The autonomous droneand the landing padcan each include communication interfacesand, respectively. The communication interfacesandcan be used to communicate with each other or one or more other systems or devices, including systems or devices that are remotely located from the autonomous droneor the landing pad. The communication interfacesandcan include any circuits, components, software, etc. for communicating with one or more networks (e.g., the network(s)). In some implementations, the communication interfacesandcan include, for example, one or more of a communications controller, receiver, transceiver, transmitter, port, conductors, software or hardware for communicating data.

1018 1026 100 200 100 200 1026 1018 100 200 100 200 In some examples, the communication interfacesandof the autonomous droneand landing padcan communicate through physical contact or wired connection while the autonomous droneis docked on the landing pad. For example, the communication interfacecan include a mechanism (e.g., data pins) to transfer data to the communication interface. In some examples, when the autonomous dronemakes contact with the landing pad(e.g., data pins) a high-speed telecommunication channel can be established to allow for communication between the autonomous droneand the landing pad.

1018 1026 100 200 100 1026 1018 100 100 1018 1026 1026 1026 1018 100 In some examples, the communication interfacesandof the autonomous droneand landing padcan communicate wirelessly as the autonomous droneflies throughout the warehouse environment. For example, the communication interfacecan emit a wireless signal (e.g., wireless local area network (WLAN)) which can be received by the communication interfaceof the autonomous droneas the autonomous droneflies throughout the warehouse environment. In some examples, a connection can be established between the communication interfacesandwhen the signal strength emitted from communication interfacereaches a certain threshold. In some examples, the communication interfacecan include a pool of internet protocol (IP) addresses that are dynamically assigned to the communication interfaceof an autonomous dronein range of the wireless signal.

1018 1026 100 1026 200 1018 100 1026 100 100 1018 1026 100 200 The communication interfacesandcan transition between contact (e.g., wired) communication and wireless communication. For example, when an autonomous dronetakes off to execute an inventory scanning mission, the communication interfaceof landing padscan beacon (e.g., regular transmissions to inform devices about available access points) via communication interfacesof autonomous drones. In some examples, the communication interfacesof the autonomous dronecan beacon every 5 seconds to detect an autonomous dronein range of the emitted signal. In some examples, the communication interfacesandcan automatically activate a contact (e.g., wired) connection when the autonomous dronedocks on a landing pad. In some examples, the contact connection can generate an ethernet connection.

1018 1026 300 200 300 100 300 1026 200 1026 200 1018 1026 200 300 1018 1026 100 200 In some examples, the communication interfacesandcan maintain a constant connection. For example, a warehouse environmentcan include multiple landing padslocated throughout the warehouse environment. When an autonomous droneflies throughout the warehouse environment, the wireless signal emitted from a first communication interfaceof a first landing padmay decrease while the wireless signal emitted from a second communication interfaceof a landing padmay increase. In some examples, the communication interfacesandmay maintain a constant connection by seamlessly switching between different landing padsas it flies throughout the warehouse environment. In some examples, the communication interfacesandcan maintain a connection when the autonomous dronedocks on a landing padand activates a contact (e.g., wired) connection.

1000 1050 1000 1052 1054 1052 1054 The computing systemcan include one or more computing devices. The computing systemcan include one or more processorsand a memory. The one or more processorscan be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memorycan include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and combinations thereof.

1054 1052 1054 1056 1056 1000 1000 The memorycan store information that can be accessed by the one or more processors. For instance, the memory(e.g., one or more non-transitory computer-readable storage media, memory devices, etc.) can store datathat can be obtained. The datacan include, for instance, any data or information described herein. In some implementations, the computing systemcan obtain data from one or more memory device(s) that are remote from the computing system.

1054 1058 1052 1050 1000 For example, the memorycan store instructionsthat are executable (e.g., by the one or more processors, by one or more other processors, etc.) to perform (e.g., with the computing device(s), the computing system, or other system(s) having processors for executing the instructions) any of the operations, functions, or methods/processes described herein.

1000 1000 In some implementations, the computing systemincludes or is otherwise implemented by one or more server computing devices. In instances in which the computing systemincludes plural server computing devices, such server computing devices can operate according to sequential computing architectures, parallel computing architectures, or some combination thereof.

1000 1060 1060 As described above, the computing systemcan store or otherwise include one or more models. For example, the modelscan be or can otherwise include various machine-learned models. Example machine-learned models include neural networks or other multi-layer non-linear models. Example neural networks include feed forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks. Some example machine-learned models can leverage an attention mechanism such as self-attention. For example, some example machine-learned models can include multi-headed self-attention models (e.g., transformer models).

10 1015 1060 1000 1002 1000 200 200 The other systems of ecosystemcan train the modelsand/orvia interaction with the computing systemthat is communicatively coupled over the networks. The computing systemcan be separate from the landing pador can be a portion of the landing pad.

1000 1062 1015 1060 1000 The computing systemcan include a model trainerthat trains the machine-learned modelsand/orstored at another computing system and/or the computing systemusing various training or learning techniques, such as, for example, backwards propagation of errors. For example, a loss function can be backpropagated through the model(s) to update one or more parameters of the model(s) (e.g., based on a gradient of the loss function). Various loss functions can be used such as mean squared error, likelihood loss, cross entropy loss, hinge loss, and/or various other loss functions. Gradient descent techniques can be used to iteratively update the parameters over a number of training iterations.

1000 1066 100 200 1002 1066 1002 The computing systemcan include communication interfacesthat can be used to communicate with the autonomous droneor the landing pad(e.g., via the network). The communication interfacescan include any circuits, components, software, etc. for communicating with one or more networks (e.g., the network(s)), such as, for example, one or more of a communications controller, receiver, transceiver, transmitter, port, conductors, software or hardware for communicating data.

1062 In some implementations, performing backwards propagation of errors can include performing truncated backpropagation through time. The model trainercan perform a number of generalization techniques (e.g., weight decays, dropouts, etc.) to improve the generalization capability of the models being trained.

1062 1015 1060 1064 1064 In particular, the model trainercan train the modelsand/orbased on a set of training data. The training datacan include, for example, labelled training data including one or more labelled features.

1062 1062 1062 1062 The model trainerincludes computer logic utilized to provide desired functionality. The model trainercan be implemented in hardware, firmware, and/or software controlling a general purpose processor. For example, in some implementations, the model trainerincludes program files stored on a storage device, loaded into a memory and executed by one or more processors. In other implementations, the model trainerincludes one or more sets of computer-executable instructions that are stored in a tangible computer-readable storage medium such as RAM, hard disk, or optical or magnetic media.

1002 1002 The network(s)can be any type of network or combination of networks that allows for communication between devices. In some implementations, the network(s) can include one or more of a local area network, wide area network, the Internet, secure network, cellular network, mesh network, peer-to-peer communication link or some combination thereof and can include any number of wired or wireless links. Communication over the network(s)can be accomplished, for instance, through a network interface using any type of protocol, protection scheme, encoding, format, packaging, etc.

Aspects of the disclosure have been described in terms of illustrative implementations thereof. Numerous other implementations, modifications, or variations within the scope and spirit of the appended claims may occur to persons of ordinary skill in the art from a review of this disclosure. Any and all features in the following claims may be combined or rearranged in any way possible. Accordingly, the scope of the present disclosure is by way of example rather than by way of limitation, and the subject disclosure does not preclude inclusion of such modifications, variations or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art. Moreover, terms are described herein using lists of example elements joined by conjunctions such as “and,” “or,” “but,” etc. It should be understood that such conjunctions are provided for explanatory purposes only. Lists joined by a particular conjunction such as “or,” for example, may refer to “at least one of” or “any combination of” example elements listed therein, with “or” being understood as “and/or” unless otherwise indicated. Also, terms such as “based on” should be understood as “based at least in part on.”

Those of ordinary skill in the art, using the disclosures provided herein, will understand that the elements of any of the claims, operations, or processes discussed herein may be adapted, rearranged, expanded, omitted, combined, or modified in various ways without deviating from the scope of the present disclosure. Some of the claims are described with a letter reference to a claim element for exemplary illustrated purposes and is not meant to be limiting.